IISc Paper Reading Session
WHAT: We will discuss a research paper in computer systems. If you want to get the most out of the session, please read the paper beforehand and come prepared to discuss, question, and engage with the ideas.
Paper details:
Title: GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecture.
Venue: ACM ASPLOS 2023
Link: https://dl.acm.org/doi/10.1145/3575693.3575748
WHO: Anyone genuinely curious about research: students, researchers, engineers, and practitioners. No prerequisites beyond curiosity and a willingness to sit with something difficult, think deeply, and discuss openly.
ABSTRACT: Graphics Processing Units (GPUs) have traditionally relied on the host CPU to initiate access to the data storage. This approach is well-suited for GPU applications with known data access patterns that enable partitioning of their dataset to be processed in a pipelined fashion in the GPU. However, emerging applications such as graph and data analytics, recommender systems, or graph neural networks, require fine-grained, data-dependent access to storage. CPU initiation of storage access is unsuitable for these applications due to high CPU-GPU synchronization overheads, I/O traffic amplification, and long CPU processing latencies. GPU-initiated storage removes these overheads from the storage control path and, thus, can potentially support these applications at much higher speed. However, there is a lack of systems architecture and software stack that enable efficient GPU-initiated storage access. This work presents a novel system architecture, BaM, that fills this gap. BaM features a fine-grained software cache to coalesce data storage requests while minimizing I/O traffic amplification. This software cache communicates with the storage system via high-throughput queues that enable the massive number of concurrent threads in modern GPUs to make I/O requests at a high rate to fully utilize the storage devices and the system interconnect. Experimental results show that BaM delivers 1.0x and 1.49x end-to-end speed up for BFS and CC graph analytics benchmarks while reducing hardware costs by up to 21.7x over accessing the graph data from the host memory. Furthermore, BaM speeds up data-analytics workloads by 5.3x over CPU-initiated storage access on the same hardware.